• The Journal of the Convergence on Culture Technology
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• v.6 no.4
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• pp.753-760
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• 2020

In this study, the structural safety of the bearing support was analysed by applying the vertical load (bearing design load) and horizontal load (horizontal force generated during an earthquake) using a precise three-dimensional numerical model. The results of stress and displacement of newly-poured concrete and welded rebars were confirmed numerically. Numerical results show that the increase in the horizontal force and the height of the beam causes the concrete cracking and the stress increase of the rebar connections due to the increase of the stress at the new concrete interface. Therefore, it was analyzed that the increase in the height of bearing support is directly related to the horizontal force and it is necessary to apply the bearing support height appropriate for the bearing support capacity. It was proposed that a method of setting the height of the bearing support suitable for the bearing capacity and determining the reinforcement by presenting the guideline with the correlation between the horizontal force acting on the bearing support and its height.

• Computational Structural Engineering
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• v.3 no.3
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• pp.141-146
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• 1990

This study is on a seismic analysis of absorber rod in KMRR Reactivity Control Mechanism. The model being studied is two coaxial tubes(control absorber rod and flow tube) immersed in the water and partially coupled(overlap) by water gap. The hydrodynamic mass effects by the water in each surrounding conditions are considered in the model. The natural frequencies, stresses and displacements of the system due to Safe Shutdown Earthquake are computed in the cases of in-phase modes and out-of-phase modes of two coaxial tubes. The results show that maximum stresses are well below the allowable limit but the maximum displacements at the ends of both tubes are so much that the absorber rod contacts with the flow tube(or surrounding wall).

• Journal of the Computational Structural Engineering Institute of Korea
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• v.26 no.3
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• pp.207-212
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• 2013

APR1000(Advanced Power Reactor 1000) was developed to export 1000MW nuclear power plants by adding ADFs(Advanced Design Features) including 60 years design life, local frequency control operation, 0.3g SSE, etc. to OPR1000(Optimized Power Reactor 1000). In this paper, environmental fatigue analyses for the reactor vessel in APR1000 have been performed as per Reg. Guide 1.207. Outlet nozzle, which has a relatively high cumulative usage factor in the reactor vessel was evaluated and a structural integrity is maintained under the reactor coolant environment.

• Journal of the Korean Geotechnical Society
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• v.38 no.11
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• pp.119-135
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• 2022

This paper used dynamic centrifuge tests to examine the seismic response for a deep temporary retaining wall with four input motions of 100, 1,000, and 2,400 years of return periods. The centrifuge model was designed based on an actual deep excavation design with a 50 m maximum excavation depth. The model backfill was prepared with dry silica sand at a relative density of 55%, and the retaining wall was modeled as a 24.8 m height diaphragm wall supported by struts. Acceleration response was amplified at the backfill surface, top of the wall, and near bedrock. However, in the middle of the model, input motion was de-amplified. The member forces of the wall and strut induced by the seismic load, which excited, were compared with the member force at rest condition. The wall's maximum negative and positive moments were increased to 36% and 10% compared to the maximum moment at rest. The maximum axial force increases to 70% of the at rest axial force on the bottom strut. The equivalent static analysis using Mononobe-Okabe (M-O) and Seed-Whitman (S-W) seismic earth pressures were compared to the centrifuge results. Considering the bending moment, the analysis results with the M-O theory underestimates but that with the S-W theory overestimates.

• Journal of Intelligence and Information Systems
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• v.27 no.2
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• pp.33-54
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• 2021

With the fourth industrial revolution and the arrival of the New Normal era due to Corona, the importance of Non-contact technologies such as artificial intelligence and big data research has been increasing. Convergent research is being conducted in earnest to keep up with these research trends, but not many studies have been conducted in the area of nuclear research using artificial intelligence and big data-related technologies such as natural language processing and text mining analysis. This study was conducted to confirm the applicability of data science analysis techniques to the field of nuclear research. Furthermore, the study of identifying trends in nuclear spent fuel recognition is critical in terms of being able to determine directions to nuclear industry policies and respond in advance to changes in industrial policies. For those reasons, this study conducted a media trend analysis of pyroprocessing, a spent nuclear fuel treatment technology. We objectively analyze changes in media perception of spent nuclear fuel dry treatment techniques by applying text mining analysis techniques. Text data specializing in Naver's web news articles, including the keywords "Pyroprocessing" and "Sodium Cooled Reactor," were collected through Python code to identify changes in perception over time. The analysis period was set from 2007 to 2020, when the first article was published, and detailed and multi-layered analysis of text data was carried out through analysis methods such as word cloud writing based on frequency analysis, TF-IDF and degree centrality calculation. Analysis of the frequency of the keyword showed that there was a change in media perception of spent nuclear fuel dry treatment technology in the mid-2010s, which was influenced by the Gyeongju earthquake in 2016 and the implementation of the new government's energy conversion policy in 2017. Therefore, trend analysis was conducted based on the corresponding time period, and word frequency analysis, TF-IDF, degree centrality values, and semantic network graphs were derived. Studies show that before the 2010s, media perception of spent nuclear fuel dry treatment technology was diplomatic and positive. However, over time, the frequency of keywords such as "safety", "reexamination", "disposal", and "disassembly" has increased, indicating that the sustainability of spent nuclear fuel dry treatment technology is being seriously considered. It was confirmed that social awareness also changed as spent nuclear fuel dry treatment technology, which was recognized as a political and diplomatic technology, became ambiguous due to changes in domestic policy. This means that domestic policy changes such as nuclear power policy have a greater impact on media perceptions than issues of "spent nuclear fuel processing technology" itself. This seems to be because nuclear policy is a socially more discussed and public-friendly topic than spent nuclear fuel. Therefore, in order to improve social awareness of spent nuclear fuel processing technology, it would be necessary to provide sufficient information about this, and linking it to nuclear policy issues would also be a good idea. In addition, the study highlighted the importance of social science research in nuclear power. It is necessary to apply the social sciences sector widely to the nuclear engineering sector, and considering national policy changes, we could confirm that the nuclear industry would be sustainable. However, this study has limitations that it has applied big data analysis methods only to detailed research areas such as "Pyroprocessing," a spent nuclear fuel dry processing technology. Furthermore, there was no clear basis for the cause of the change in social perception, and only news articles were analyzed to determine social perception. Considering future comments, it is expected that more reliable results will be produced and efficiently used in the field of nuclear policy research if a media trend analysis study on nuclear power is conducted. Recently, the development of uncontact-related technologies such as artificial intelligence and big data research is accelerating in the wake of the recent arrival of the New Normal era caused by corona. Convergence research is being conducted in earnest in various research fields to follow these research trends, but not many studies have been conducted in the nuclear field with artificial intelligence and big data-related technologies such as natural language processing and text mining analysis. The academic significance of this study is that it was possible to confirm the applicability of data science analysis technology in the field of nuclear research. Furthermore, due to the impact of current government energy policies such as nuclear power plant reductions, re-evaluation of spent fuel treatment technology research is undertaken, and key keyword analysis in the field can contribute to future research orientation. It is important to consider the views of others outside, not just the safety technology and engineering integrity of nuclear power, and further reconsider whether it is appropriate to discuss nuclear engineering technology internally. In addition, if multidisciplinary research on nuclear power is carried out, reasonable alternatives can be prepared to maintain the nuclear industry.